First part of the proposed framework encodes an input image \(X^k\)  into a feature-cube \(F^k\) . All the input images are processed through the LR-CNN in a patch based manner. The proposed framework is flexible enough to use any state-of-the-art image classifier as a LR-CNN such as ResNet50 [1]}, MobileNet [2]}, Inception [3]}, or Xception [4]}. This flexibility also enables it to use pre-trained weights in case of a limited dataset. Moreover, it is possible to train the LR-CNN independently before plugging it into the proposed framework, enabling it to learn meaningful representation [5]} which leads to early convergence of the context-aware learning part of the framework.
